The adaptive sliding mode control using improved genetic algorithm tuning PID controller for the planetary rover

Author:

Yuan Tiger,Guo Guanyou,Du Baiyu,Zhao Zhiping,Xu Weikai

Abstract

Purpose The purpose of this paper is to resolve the problem of the dynamic response performance of the driving control system for a six-wheeled planetary rover. An adaptive sliding mode controller based on an improved genetic algorithm (IGA) to tune PID sliding surface parameters was used in the driving control system of the planetary rover. Design/methodology/approach First, the mathematical model of planetary rover driving control is established. Second, according to sliding mode variable structure control, an equivalent controller and a disturbance controller are constructed to solve the problem of a multi-disturbance nonlinear driving control system of planetary rovers and an IGA is used to tune PID parameters. Findings Simulation results show that the proposed control algorithm improves the accuracy of the driving control system and optimizes the smoothness of rover motion control. Practical implications The controller based on the IGA to tune PID sliding surface parameters has good self-adaptability and real-time controllability for the control object which is difficult to present a precise mathematical model. Originality/value The advanced control method is adopted to solve the uncertainty and external interference of planetary rovers in a complex environment. The mathematical model of the six-wheeled rover is established as the control object and the uncertainty and external disturbance of the model are considered. The controller based on IGA has good adaptability and real-time performance and the control algorithm can be used to drive robots in complex environments.

Publisher

Emerald

Subject

Aerospace Engineering

Reference23 articles.

1. Neural network-based self-tuning PID control for underwater vehicles;Sensors,2016

2. Unsupervised classification of slip events for planetary exploration rovers;Journal of Terramechanics,2017

3. Incremental Q -learning strategy for adaptive PID control of mobile robots;Expert Systems with Applications,2017

4. Optimization of PID controller parameters based on improved genetic algorithm;Applied Mechanics and Materials,2013

5. Sliding mode controller design with optimized PID sliding surface using particle swarm algorithm;Procedia Computer Science,2017

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